Character-Word LSTM Language Models
Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq · 2017
We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model.Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words.By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch.Moreover, we also outperform baseline word-level models with a larger number of parameters.